Fixed-Point MAP Decoding of Channel Codes

This paper describes the fixed-point model of the maximum a posteriori (MAP) decoding algorithm of turbo and low-density parity-check (LDPC) codes, the most advanced channel codes adopted by modern communication systems for forward error correction (FEC). Fixed-point models of the decoding algorithms are developed in a unified framework based on the use of the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. This approach aims at bridging the gap toward the design of a universal, multistandard decoder of channel codes, capable of supporting the two classes of codes and having reduced requirements in terms of silicon area and power consumption and so suitable to mobile applications. The developed models allow the identification of key parameters such as dynamic range and number of bits, whose impact on the error correction performance of the algorithm is of pivotal importance for the definition of the architectural tradeoffs between complexity and performance. This is done by taking the turbo and LDPC codes of two recent communication standards such as WiMAX and 3GPP-LTE as a reference benchmark for a mobile scenario and by analyzing their performance over additive white Gaussian noise (AWGN) channel for different values of the fixed-point parameters